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Kernel Conditional Exponential Family

Arbel, M; Gretton, AL; (2018) Kernel Conditional Exponential Family. In: Proceedings of the 21st International Conference on Artifi- cial Intelligence and Statistics (AISTATS) 2018. (pp. pp. 1337-1346). PMLR Green open access

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Abstract

A nonparametric family of conditional distributions is introduced, which generalizes conditional exponential families using functional parameters in a suitable RKHS. An algorithm is provided for learning the generalized natural parameter, and consistency of the estimator is established in the well specified case. In experiments, the new method generally outperforms a competing approach with consistency guarantees, and is competitive with a deep conditional density model on datasets that exhibit abrupt transitions and heteroscedasticity.

Type: Proceedings paper
Title: Kernel Conditional Exponential Family
Event: AISTATS 2018
Open access status: An open access version is available from UCL Discovery
Publisher version: http://proceedings.mlr.press/v84/arbel18a.html
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10051226
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